题 目：Compressive Sensing Based Wideband Spectrum Sensing
时 间：10月16日（周一）上午10:00-11:30
地 点：信电楼117/119
报告人：Dr. Zhijin Qin
Lancaster University, Lancaster, U.K.
Abstract
One of the crucial requirement in cognitive radio networks is wideband spectrum sensing, in which secondary users (SUs) should detect spectral opportunities over a wide frequency range. However, wideband spectrum sensing usually leads to unaffordable high sampling rates at energy-constrained SUs. As the spectrum utilization is low in practice, signals over a wide frequency band naturally exhibit sparsity property in the frequency domain. By exploiting sparse property, compressive sensing has been developed to overcome this issue, which enables sub-Nyquist sampling and therefore significantly reduces sampling rates. This talk will cover the existing work on compressive sensing based wideband sensing and the research challenges in the area.
Biographies
Zhijin Qin received her Bachelor degree from Beijing University of Posts and Telecommunications in China in 2012, and her Ph.D degree from Queen Mary University of London in UK in 2016. She has been with Lancaster University in UK as a Lecturer (Assistant Professor) since August 2017. Before that, she was with Imperial College London as a Research Associate. Her research interests include compressive sensing and machine learning in wireless communications, low-power wide-area network for smart cities, and non-orthogonal multiple access in 5G networks. She won the best paper award at Wireless Technology Symposium 2012.
